Regional socioeconomic assessments with a genetic algorithm: an application on income inequality across municipalities

E. Aracil, E. Diaz-Aguiluz, G. Gomez-Bengoechea, R. Mota, D. Roch Dupré

Social Indicators Research Vol. 173, pp. 499 - 521

Summary:

Available data to depict socioeconomic realities are often scarce at the municipal level. Unlike recurring or continuous data, which are collected regularly or repeatedly, nonrecurrent data may be sporadic or irregular, due to significant costs for their compilation and limited resources at municipalities. To address regional data scarcity, we develop a bottom-up top-down methodology for constructing synthetic socioeconomic indicators combining a genetic algorithm and regression techniques. We apply our methodology for assessing income inequalities at 178 municipalities in Spain. The genetic algorithm draws the available data on circumstances or inequalities of opportunities that give birth to income disparities. Our methodology allows to mitigate the shortcomings arising from unavailable data. Thus, it is a suitable method to assess relevant socioeconomic conditions at a regional level that are currently obscured due to data unavailability. This is crucial to provide policymakers with an enhanced socioeconomic overview at regional administrative units, relevant to allocating public service funds.

Spanish layman's summary:

Desarrollamos una metodología para abordar la escasez de datos socioeconómicos. Al combinar un algoritmo genético y técnicas de regresión, creamos indicadores sintéticos para evaluar desigualdades, ayudando a tomar decisiones informadas sobre sostenibilidad y asignación de recursos.

English layman's summary:

We develop a methodology to address the scarcity of socioeconomic data. By combining a genetic algorithm and regression techniques, we create synthetic indicators to assess inequalities, aiding informed decisions on sustainability and resource allocation.

Keywords: Income inequality · Inequality of opportunities · Genetic algorithm · Socioeconomic indicator · Data scarcity · Municipalities

JCR Impact Factor and WoS quartile: 3,100 - Q1 (2022)

DOI reference: https://doi.org/10.1007/s11205-024-03345-4

Published on paper: June 2024.

Published on-line: May 2024.

Citation:
E. Aracil, E. Diaz-Aguiluz, G. Gomez-Bengoechea, R. Mota, D. Roch Dupré, Regional socioeconomic assessments with a genetic algorithm: an application on income inequality across municipalities. Social Indicators Research. Vol. 173, pp. 499 - 521, June 2024. [Online: May 2024]